Imagine a world where you can't tell if a video of a politician or CEO is real or a computer-generated fake. This work figures out why our brains trust these fakes and how to spot them. It also explores how to use this same AI tech to create positive, helpful messages instead of lies.
Companies and governments struggle to distinguish AI-generated deepfakes from reality, leading to disinformation and trust erosion. There is a lack of technical and psychological tools to mitigate these risks in real-time.
A psychometric scale of perceived trustworthiness for deepfakes and a set of simulation protocols for viral deepfake spreading.
If you are a verification firm dealing with identity theft via AI-generated video — this project developed a psychometric scale of perceived trustworthiness that helps identify why users believe fakes, allowing for better detection tools.
If you are a news agency dealing with viral disinformation — this project developed simulation protocols for the spreading of deepfakes that help organizations create mitigation strategies to protect their reputation.
If you are a marketing agency dealing with low citizen engagement on global issues — this project developed value-based GAN contents that use AI to create inclusive and constructive messages for the public.
Based on available project data, there is no specific pricing or commercial cost listed for the resulting tools.
The project uses use-cases and simulations to test protocols, but based on available project data, it has not yet reached full industrial scale.
Based on available project data, specific licensing terms or patent details are not provided.
The project focuses on establishing regulatory innovations to detect and mitigate deepfake risks to protect democratic governance.
The project period runs from 2023-02-01 to 2026-01-31.
The consortium is heavily weighted toward academic research with 7 universities and 2 research institutes. However, there is a significant business presence with 3 industry partners, including 2 SMEs, representing a 23% industry ratio. This suggests the project is primarily research-driven but has a clear path toward commercial application through its industry members across 8 countries.
Universiteit Utrecht
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